import numpy as np

class GULP:
	def __init__(self):
		self.ver="2014.01.28"
GULP.pbc= np.array([ True, True, True], dtype=bool)
GULP.cell= np.array([
	[8.93076,0,0],
	[0,5.95384,0],
	[0,0,23.42]])
GULP.scaled_positions= np.array([
	[0.578633,0.302123,0.218589],
	[0.762789,0.553684,0.312622],
	[0.078391,0.553354,0.313708],
	[0.242805,0.801983,0.220219],
	[0.078509,0.053341,0.313727],
	[0.578346,0.802044,0.218543],
	[0.764646,0.052150,0.315978],
	[0.914503,0.803499,0.220226],
	[0.392376,0.552233,0.316353],
	[0.394067,0.053759,0.312774],
	[0.242762,0.303564,0.220285],
	[0.245310,0.302458,0.038895],
	[0.245310,0.802458,0.038895],
	[0.411977,0.552458,0.128775],
	[0.578643,0.302458,0.038895],
	[0.745310,0.552458,0.128775],
	[0.911977,0.302458,0.038895],
	[0.078643,0.552458,0.128775],
	[0.411977,0.052458,0.128775],
	[0.578643,0.802458,0.038895],
	[0.745310,0.052458,0.128775],
	[0.914475,0.302009,0.220162],
	[0.078643,0.052458,0.128775],
	[0.911977,0.802458,0.038895],
	[0.078492,0.552780,0.218146],
	[0.411977,0.552458,0.038895],
	[0.245310,0.302458,0.128775],
	[0.415972,0.552297,0.221912],
	[0.242061,0.301936,0.308629],
	[0.745310,0.552458,0.038895],
	[0.578643,0.302458,0.128775],
	[0.578643,0.802458,0.128775],
	[0.078643,0.552458,0.038895],
	[0.914796,0.801909,0.308540],
	[0.078720,0.052788,0.218148],
	[0.911977,0.302458,0.128775],
	[0.911977,0.802458,0.128775],
	[0.581783,0.810464,0.317617],
	[0.741146,0.052266,0.221897],
	[0.741047,0.552580,0.221632],
	[0.411977,0.052458,0.038895],
	[0.241882,0.804159,0.308031],
	[0.416074,0.052526,0.221674],
	[0.245310,0.802458,0.128775],
	[0.574803,0.310712,0.317542],
	[0.914951,0.304125,0.307963],
	[0.745310,0.052458,0.038895],
	[0.078643,0.052458,0.038895],
	[0.757565,0.112387,0.403745],
	[0.402991,0.045866,0.405236],
	[0.049889,0.052280,0.409081],
	[0.753518,0.547187,0.405140],
	[0.396680,0.611408,0.404173],
	[0.105031,0.551945,0.409016],
	[0.584710,0.278854,0.358688],
	[0.395156,0.874639,0.412279],
	[0.308403,0.111374,0.417954],
	[0.068666,0.897103,0.421405],
	[0.935296,0.070960,0.411779],
	[0.760088,0.375955,0.412217],
	[0.848861,0.610761,0.417739],
	[0.574263,0.780566,0.358908],
	[0.459111,0.533796,0.431941],
	[0.086253,0.396488,0.421130],
	[0.219711,0.570635,0.411906],
	[0.694566,0.034707,0.431325],
	[0.578643,0.802458,0.038797],
	[0.578643,0.302458,0.038797],
	[0.911977,0.302458,0.038794],
	[0.245311,0.802458,0.038794],
	[0.911977,0.802458,0.038794],
	[0.245311,0.302458,0.038794],
	[0.578643,0.802458,0.038876],
	[0.578643,0.802461,0.038876],
	[0.578644,0.302458,0.038876],
	[0.762590,0.553685,0.312630],
	[0.911977,0.302458,0.038875],
	[0.394266,0.053760,0.312783],
	[0.911977,0.302461,0.038875],
	[0.911976,0.802458,0.038875],
	[0.245311,0.802458,0.038875],
	[0.762731,0.553684,0.312645],
	[0.578643,0.302461,0.038876],
	[0.394125,0.053759,0.312797],
	[0.762671,0.553678,0.312630],
	[0.245312,0.802458,0.038875],
	[0.394185,0.053753,0.312783],
	[0.245310,0.302458,0.038875],
	[0.911976,0.802459,0.038875],
	[0.245311,0.302457,0.038875],
	[0.578346,0.802047,0.218607],
	[0.578633,0.302125,0.218653],
	[0.764462,0.052175,0.315979],
	[0.392558,0.552257,0.316354],
	[0.764594,0.052142,0.315966],
	[0.392428,0.552226,0.316339],
	[0.764532,0.052177,0.315980],
	[0.578347,0.802049,0.218554],
	[0.392489,0.552260,0.316355],
	[0.578347,0.802047,0.218555],
	[0.578634,0.302125,0.218601],
	[0.578633,0.302127,0.218601],
	[0.411987,0.052458,0.128780],
	[0.745305,0.552455,0.128780],
	[0.411983,0.052459,0.128780],
	[0.745300,0.552458,0.128780],
	[0.745307,0.552458,0.128796],
	[0.411981,0.052458,0.128796],
	[0.745301,0.052458,0.128780],
	[0.745307,0.052458,0.128796],
	[0.411981,0.552458,0.128796],
	[0.411987,0.552458,0.128780],
	[0.745305,0.052457,0.128780],
	[0.411982,0.552457,0.128780],
	[0.078511,0.053341,0.313752],
	[0.078388,0.553354,0.313732],
	[0.078502,0.053340,0.313736],
	[0.078396,0.553353,0.313717],
	[0.078506,0.053340,0.313737],
	[0.078393,0.553352,0.313718],
	[0.078643,0.552459,0.128773],
	[0.078644,0.552458,0.128773],
	[0.242810,0.303559,0.220278],
	[0.914475,0.803492,0.220219],
	[0.078642,0.052458,0.128773],
	[0.914455,0.803494,0.220219],
	[0.242790,0.303557,0.220278],
	[0.078643,0.552458,0.128770],
	[0.078643,0.052458,0.128770],
	[0.078643,0.052459,0.128773],
	[0.914488,0.803498,0.220198],
	[0.242777,0.303562,0.220257],
	[0.914424,0.302012,0.220154],
	[0.242855,0.801987,0.220211],
	[0.914446,0.302013,0.220154],
	[0.242834,0.801986,0.220211],
	[0.914459,0.302009,0.220129],
	[0.242821,0.801984,0.220186],
	[0.996794,0.059399,0.410830],
	[0.803899,0.578602,0.412019],
	[0.352930,0.078276,0.412185],
	[0.088093,0.586902,0.417438],
	[0.578312,0.794943,0.338927],
	[0.579504,0.294241,0.338742],
	[0.723907,0.070613,0.418864],
	[0.430090,0.569701,0.419400],
	[0.576790,0.789427,0.113684],
	[0.723731,0.572322,0.234741],
	[0.894061,0.819019,0.115499],
	[0.263728,0.825598,0.141129],
	[0.578842,0.289412,0.113668],
	[0.434016,0.031505,0.208849],
	[0.914592,0.793065,0.293164],
	[0.720604,0.027423,0.210156],
	[0.779808,0.573379,0.032731],
	[0.894437,0.286211,0.115397],
	[0.263304,0.319083,0.115521],
	[0.436029,0.576978,0.210009],
	[0.044506,0.073692,0.032447],
	[0.377474,0.573391,0.032738],
	[0.377474,0.031515,0.032739],
	[0.043590,0.570362,0.212130],
	[0.046441,0.033777,0.210385],
	[0.217390,0.264024,0.315149],
	[0.934815,0.345149,0.315572],
	[0.711637,0.074270,0.032225],
	[0.044512,0.531200,0.032444],
	[0.221941,0.845030,0.315680],
	[0.058710,0.972339,0.415913],
	[0.397767,0.967641,0.408916],
	[0.758140,0.468821,0.408850],
	[0.158012,0.559108,0.410867],
	[0.614726,0.786143,0.312741],
	[0.573037,0.365944,0.316950],
	[0.066607,0.087437,0.417496],
	[0.402730,0.670215,0.406653],
	[0.727604,0.567425,0.412297],
	[0.751417,0.171121,0.406213],
	[0.429126,0.065534,0.412388],
	[0.583706,0.865684,0.316871],
	[0.096364,0.552601,0.394728],
	[0.412178,0.109793,0.037209],
	[0.578141,0.858625,0.130008],
	[0.548727,0.791423,0.311532],
	[0.612925,0.784501,0.135035],
	[0.607439,0.291624,0.311094],
	[0.262909,0.818773,0.115414],
	[0.909350,0.746261,0.129611],
	[0.950665,0.814183,0.128549],
	[0.925290,0.857654,0.310733],
	[0.247925,0.246255,0.129592],
	[0.744576,0.562302,0.053813],
	[0.079121,0.496530,0.216832],
	[0.430491,0.569854,0.235636],
	[0.420484,0.496066,0.220936],
	[0.745086,0.995120,0.037218],
	[0.231763,0.357793,0.310738],
	[0.078645,0.609798,0.037245],
	[0.876555,0.795905,0.313466],
	[0.737341,0.496398,0.221404],
	[0.752815,0.553276,0.390244],
	[0.780254,0.562739,0.221464],
	[0.096203,0.471866,0.415733],
	[0.445675,0.574225,0.032235],
	[0.058753,0.052757,0.394812],
	[0.403668,0.052108,0.390347],
	[0.722913,0.573982,0.208881],
	[0.726737,0.035883,0.235720],
	[0.109766,0.573345,0.210024],
	[0.112772,0.073726,0.032445],
	[0.280242,0.295762,0.313589],
	[0.541503,0.286921,0.312888],
	[0.893945,0.825572,0.141225],
	[0.546898,0.782224,0.136454],
	[0.779834,0.073323,0.032745],
	[0.950651,0.290673,0.128699],
	[0.112797,0.033698,0.211722],
	[0.376869,0.042463,0.221556],
	[0.893548,0.279145,0.141111],
	[0.409397,0.595874,0.390170],
	[0.611596,0.283273,0.135833],
	[0.744998,0.097009,0.389705],
	[0.874692,0.308750,0.310316],
	[0.263273,0.325489,0.141247],
	[0.780336,0.042171,0.220607],
	[0.282126,0.808855,0.310388],
	[0.578683,0.358627,0.129991],
	[0.796263,0.098629,0.406528],
	[0.357856,0.597589,0.406921],
	[0.545440,0.283472,0.135684],
	[0.081321,0.565949,0.233056],
	[0.909362,0.358643,0.129673],
	[0.247867,0.746252,0.129643],
	[0.078632,0.542466,0.053776],
	[0.206622,0.314198,0.128519],
	[0.744552,0.062291,0.053812],
	[0.737066,0.108461,0.220763],
	[0.412723,0.562263,0.053812],
	[0.206643,0.814325,0.128688],
	[0.078641,0.062460,0.053775],
	[0.412717,0.042670,0.053814],
	[0.419799,0.108674,0.221530],
	[0.711609,0.574203,0.032237],
	[0.939263,0.763593,0.315007],
	[0.235354,0.811689,0.292756],
	[0.433566,0.032420,0.234706],
	[0.921456,0.311510,0.292684],
	[0.925435,0.249007,0.311302],
	[0.445677,0.030668,0.032239],
	[0.242275,0.292739,0.293265],
	[0.745114,0.495120,0.037221],
	[0.376713,0.561577,0.220685],
	[0.078651,0.995119,0.037247],
	[0.412176,0.495125,0.037213],
	[0.076654,0.039620,0.233075],
	[0.231478,0.748991,0.311331],
	[0.112779,0.531196,0.032450],
	[0.078948,0.109032,0.216867]])
GULP.charges=   np.array([	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	8.00000,
	6.00000,
	6.00000,
	6.00000,
	6.00000,
	6.00000,
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	6.00000,
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	6.00000,
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	6.00000,
	1.00000,
	1.00000,
	1.00000,
	1.00000,
	1.00000,
	1.00000,
	1.00000,
	1.00000,
	1.00000,
	1.00000,
	1.00000,
	1.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
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	-2.00000,
	-2.00000,
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	-2.00000,
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	-2.00000,
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	-2.00000,
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	-2.00000,
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	-2.00000,
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	-2.00000,
	-2.00000,
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	-2.00000,
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	-2.00000,
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	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
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	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
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	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
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	-2.00000,
	-2.00000,
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	-2.00000,
	-2.00000,
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	-2.00000,
	-2.00000,
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	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
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	-2.00000,
	-2.00000,
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	-2.00000,
	-2.00000,
	-2.00000,
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	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	-2.00000,
	 -2.00000])
GULP.symbols=   np.array(['Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','Mg','O','O','O','O','O','O','O','O','O','O','O','O','O','O','O','O','O','O','O','O','O','O','O','O','O','O','O','O','O','O','H','H','H','H','H','H','H','H','H','H','H','H','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X','X'])
GULP.positions= np.array([
	[5.167635,1.798789,5.119363],
	[6.812287,3.296547,7.321596],
	[0.700087,3.294579,7.347034],
	[2.168436,4.774876,5.157527],
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GULP.EPOTgradient= np.array([
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